Measures of dissimilarity are used in a wide variety of image processing applications. Dissimilarity measures are relied upon in a variety of computer vision tasks, including image matching, image classification, image segmentation, and image retrieval. For example, measures of image dissimilarity have been incorporated in motion estimation algorithms and algorithms for matching corresponding pixels in a pair of stereoscopic images.
In general, a dissimilarity measure quantifies the degree to which two objects differ from one another. Typically, a dissimilarity measure is computed from features (or parameters) that describe the objects. With respect to image objects (or simply “images”), corresponding pixels in different images (e.g., a pair of stereoscopic images or successive video frames) of the same scene typically have different values. Many factors, such as sensor gain, bias, noise, depth discontinuities, and sampling, contribute to the dissimilarity between corresponding pixel values in different images of the same scene.
A variety of pixel dissimilarity measures have been proposed. Some of such pixel dissimilarity measures are insensitive to gain, bias, noise, and depth discontinuities. One one-dimensional pixel dissimilarity measure that has been proposed is insensitive to image sampling. This pixel dissimilarity measure uses the linearly interpolated intensity functions surrounding pixels in the left and right images of a stereoscopic pair. In particular, this approach measures how well the intensity of a pixel in the left image of the stereoscopic pair fits into the linearly interpolated region surrounding a pixel along the epipolar line in the right image of the stereoscopic pair. Similarly, this approach also measures how well the intensity of the pixel in the right image fits into the linearly interpolated region surrounding the respective pixel in the left image. The dissimilarity between the pixels is defined as the minimum of the two measurements.